High and rising house prices generally encourage consumer spending via the wealth effect, increase access to finance by small business owners by increasing available collateral, and bolster state government revenues. At the same time, rising house prices raise concerns about the bursting of house price bubbles and worsening levels of inequality. Recent attempts to change the tax treatment of property through changes to negative gearing and the capital gains tax discount revealed the very difficult trade-offs involved in this important area of public policy. Understanding the relationships between house prices and the macroeconomy is therefore important for all levels of government, the public service, individuals, investors, and the corporate sector.
The literature provides conflicting conclusions regarding the determinants of real house prices over time. One possible explanation is that certain variables affect prices more significantly in the short term, while others may have a stronger relationship with house prices over the long term. In the first part of the paper, we use the Flexible Fourier Form, a periodic (or repeating) semi-nonparametric global approximator of non-linear functions, to decompose real house prices into a smooth long-term trend component and the remaining short-term dynamics. We identify stable long-run (or cointegrating) relationships between the long-term component and income (real disposable income and real GDP). This is not the case when income is on a per capita basis. This is consistent with growth in real house prices outpacing growth in income per person. It is also consistent with a non-trivial part of disposable income and GDP growth being due to population growth. Results also show that the FFF decomposition reveals cointegrating relations with rent and affordability measures that are undetected when using raw house prices. This suggests that the removal of short-term (possibly noisy) variations may help to better identify long-run relationships between macroeconomic variables. In contrast, we show that the short-term component is largely cyclical and strongly related to sales volumes (but not disposable income or real GDP).
An alternative explanation for the lack of consensus regarding house price determinants may be failure to control for structural breaks. Breaks may occur in house prices as well as in their relationship with other macroeconomic variables. Inadequately controlling for breaks may result in significant parameter bias and poor public policy. Even though house prices have largely followed an increasing trajectory over the period examined, they have been exposed to many significant events, including the October 1987 equity market crash, the global financial crisis, and the COVID-19 pandemic. They have also been affected by policy changes over the period, like the significant rise in interest rates in the early 1990s, increases in the capital gains discount in 1999, various federal and state home buyer grant schemes, and declining investment in public housing. These events and policy changes are likely to have caused structural changes to the dynamics of house prices and their relationship to other macroeconomic variables. Some of the described events are sudden and relatively short lived, like October 1987, while others are much slower and more permanent, like the run down in public housing. Either way, housing market frictions like stamp duty and search costs mean that structural change is likely to occur more slowly in the housing market than in financial markets.
In the second part of the paper, we therefore allow for structural change in Australian house prices via the FFF, as it can be used to control for structural breaks and non‑linearities (Enders and Jones 2016; Baillie and Morana 2012). It does not require breakpoint identification procedures and is effective when there are an unknown number of breaks. Further, its flexibility means that it can capture both smooth and slow-moving structural change or much more rapid sharp adjustments (Jones and Enders 2014).1 It therefore seems natural to consider application of the FFF when examining the dynamics of the Australian property market. To our knowledge, this is the first paper to do so.
We apply the FFF as a deterministic regressor within three multivariate models of real house prices, the interbank overnight cash rate, disposable income per capita, housing sales volume, and the unemployment rate. By allowing for time variation in each of the intercept terms via their own FFF, these models allow for smooth structural change and reversion of each variable to its own time varying value. This is in contrast to a standard VAR or VECM, which has a time invariant intercept and reversion of each variable to a constant unconditional mean. We show that controlling for structural change via the FFF results in much richer interaction between variables through Granger-causality tests and Impulse Response Functions. This is consistent with Enders and Jones (2016) who apply a similar approach to US grain and oil prices.
Section 2 of the paper outlines the data employed. Section 3 presents the decomposition of real house prices into long-term and short-term components and explores their relationship with macroeconomic variables. Section 4 specifies three multivariate models with the FFF as an intercept term and explores the implications for Granger-causality testing and impulse response analysis. Section 5 concludes.
Footnotes
[1] This is in contrast to other methods like Bai-Perron (1998) where all breaks are sharp.
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